1. Field of the Invention
The present invention relates to image processing, and more particularly, to a method and apparatus for sectioning an image into a plurality of regions.
2. Description of the Related Art
Important factors in forming an image include color, illumination, shape, texture, positions of the objects, mutual geometry of the objects, and the position of the observer relative to an image forming apparatus. Image formation is affected by various devices and environmental conditions. Ideal image segmentation involves effectively distinguishing a meaningful object or a region of the same color from other objects or background in a form similar to what humans can recognize, regardless of the above conditions for image formation. A number of conventional image segmentation techniques for accomplishing this have been suggested.
One of the conventional image segmentation techniques is disclosed in “Distribution Free Decomposition of Multivariate data” [D. Comaniciu and P. Meer, Pattern Analysis and Applications, 1999, Vol, 2, pp 22-30]. The conventional image segmentation technique disclosed therein includes the use of a point-clustering algorithm. The point-clustering algorithm is applied to detect a distribution mode among distributions of color points in a color space, that is, groups of pixels having a color similar to that of a target pixel in an image using color information about the target pixel and obtain a region of the same color by projecting all pixels spaced apart within a predetermined distance onto the image. While this conventional image segmentation technique can quickly distinguish a region of the same color, it has a problem in that an image is excessively segmented in a region containing a texture. Furthermore, another problem encountered by the conventional image segmentation is that it is difficult to properly distinguish a region of a three-dimensional object having shades by insufficiently considering a shading effect.
Another conventional image segmentation approach for considering a shading effect is disclosed in U.S. Pat. No. 5,933,524 entitled “Method for Segmentation of Digital Color Images”. This conventional approach includes the use of line-clustering algorithm, in which color histograms are used for segmentation of a color image. The conventional image segmentation disclosed in the above cited reference involves obtaining a color histogram for a specific object and converting the color histogram into a normalized color histogram. The reason is to reduce changes in a color histogram of an object that may occur in a light source under other conditions. Then, thus-obtained color clusters in the color space are described as a parametric function by three models. Here, a parametric function that defines a specific object is used for a measurement of distances between locations of color pixels in the color space thereby effectively distinguishing the specific object in the image. However, this conventional image segmentation approach has limitations in segmenting regions of actual image having various color distribution. Furthermore, the conventional image segmentation has problems in that complicated computations are performed for calculating eigenvalues required for producing a model which is most suitable for representing distributions in a three-dimensional space, and therefore a time taken for segmentation of color image becomes longer.